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The availability of digital technology in the hands of every citizenry worldwide makes an available unprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the high number of free BDA tools, platforms, and data mining tools makes it challenging to select the appropriate one for the right task. This paper presents a comprehensive mini-literature review of ML in BDA, using a keyword search; a total of 1512 published articles was identified. The articles were screened to 140 based on the study proposed novel taxonomy. The study outcome shows that deep neural networks (15%), support vector machines (15%), artificial neural networks (14%), decision trees (12%), and ensemble learning techniques (11%) are widely applied in BDA. The related applications fields, challenges, and most importantly the openings for future research, are detailed.


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A Mini-Review of Machine Learning in Big Data Analytics: Applications, Challenges, and Prospects

Show Author's information Isaac Kofi Nti( )Juanita Ahia QuarcooJustice AningGodfred Kusi Fosu
Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani BS2103, Ghana
Department of Electrical & Electronic Engineering, Sunyani Technical University, Sunyani BS2103, Ghana
Department of Computer Science, Sunyani Technical University, Sunyani BS2103, Ghana

Abstract

The availability of digital technology in the hands of every citizenry worldwide makes an available unprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the high number of free BDA tools, platforms, and data mining tools makes it challenging to select the appropriate one for the right task. This paper presents a comprehensive mini-literature review of ML in BDA, using a keyword search; a total of 1512 published articles was identified. The articles were screened to 140 based on the study proposed novel taxonomy. The study outcome shows that deep neural networks (15%), support vector machines (15%), artificial neural networks (14%), decision trees (12%), and ensemble learning techniques (11%) are widely applied in BDA. The related applications fields, challenges, and most importantly the openings for future research, are detailed.

Keywords:

Big Data Analytics (BDA), Machine Learning (ML), Big Data (BD), Hadoop, MapReduce
Received: 14 October 2021 Revised: 11 December 2021 Accepted: 13 December 2021 Published: 25 January 2022 Issue date: June 2022
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Received: 14 October 2021
Revised: 11 December 2021
Accepted: 13 December 2021
Published: 25 January 2022
Issue date: June 2022

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